Abstract

Abstract

The Vehicle Routing Problem (VRP) is a well-known optimization problem in which we aim traditionally to minimize transportation costs while satisfying customer demands. In fact, most logistics companies use a heterogeneous fleet with varying capacities and costs, presenting a more complex variant known as Rich VRP (RVRP). In this paper, we present a mathematical formulation of the RVRP, considering both hard time windows and dynamically changing requests to be as close as possible to real-life logistics scenarios. To solve this challenging problem, we propose a Hybrid Genetic Algorithm (HGA). The experimental study highlights the out-performance of our proposal when evaluated alongside other algorithms on the same benchmark problems. Additionally, we conduct a sensitivity analysis to illustrate how resilient the algorithm is when problem parameters are altered.

Abstract

The application of Artificial Intelligence (AI) in
automatic sleep scoring presents significant opportunities for
enhancing sleep analysis and diagnosing sleep disorders.
However, a major challenge lies in the lack of transparency in
AI-driven decision-making, which can hinder trust and
comprehension among sleep researchers and clinicians.
Explainable Artificial Intelligence (XAI) has emerged as a key
approach to addresss these concerns by providing insights into
AI model predictions and improving interpretability. This
review examines the role and effectiveness of Explainability and
interpretability in automatic sleep scoring, analyzing key
challenges, the impact of various methodologies, and commonly
used algorithms. Based on a comprehensive analysis of 100
recent studies, we bridge the gap between computer-readable
data encodings and human-understandable information,
enhancing model explainability and transparency. Ultimately,
this review underscores the vital role of Explainability in
refining sleep evaluation and decision-making, emphasizing the
necessity of further research to address existing challenges and
maximize its potential.

Abstract

Explainable AI Planning (XAIP) is a pivotal research
area focused on enhancing the transparency, interpretability,
and trustworthiness of automated planning systems. This
paper provides a comprehensive review of XAIP, emphasizing key
techniques for plan explanation, such as contrastive explanations,
hierarchical decomposition, and argumentative reasoning frameworks.
We explore the critical role of argumentation in justifying
planning decisions and address the challenges of replanning in
dynamic and uncertain environments, particularly in high-stakes
domains like healthcare, autonomous systems, and logistics.
Additionally, we discuss the ethical and practical implications
of deploying XAIP, highlighting the importance of human-AI
collaboration, regulatory compliance, and uncertainty handling.
By examining these aspects, this paper aims to provide a detailed
understanding of how XAIP can improve the transparency,
interpretability, and usability of AI planning systems across
various domains.

Abstract

Mammogram images are subject to various types of noise, which restricts the analysis of images and diagnosis. Mammogram image denoising is very important to improve image quality and to make the segmentation and classification results more correct. In this work, we propose a Deep Convolutional Neural Network (DCNN) to denoise the mammogram images in order to improve the image quality by handling Gaussian, Speckle, Poisson, and Salt and Pepper noise. The main objective of this study is to remove different types of noises from mammogram images and to maximize the quantity of information content in the enhanced images. We first add noise models to mammogram images and then enhance the image by removing the noise using DCNN. Furthermore, we compare our results with state-of-the-art denoising methods, such as the Adaptive Median filter, Wiener filter, Gaussian filter, Median filter, and Mean. Three datasets have been used, including Digital Database for Screening Mammography (DDSM), mini-Mammographic Image Analysis Society (mini-MIAS), and a local Tunisian dataset. The experimental results show that DCNN has a better denoising performance than the other methods, with an average PSNR range of 46.0-51.83 dB and an average SSIM range of 0.988-99.83, which may suggest its adaptability to different models of noise.

Abstract

To overcome Climate Change, countries are turning to greener transportation systems. Therefore, the use of Electric Vehicles (EVs) is leveraging substantially since they present multiple advantages, like reducing hazardous emissions. Recently, the demand for EVs has increased, which means that more charging stations need to be available. By the year 2030, 15 million EVs will be accessible, and since the number of charging stations is limited, the charging needs should be defined for better management of the charging infrastructure. In this research, we aim to tackle this problem by efficiently predicting the energy consumption of EVs. We proposed a Genetic Algorithm (GA) based Three HyperParameter optimization of Deep Long Short Term Memory (GA3P-DLSTM), which is an optimized LSTM model that incorporates a GA for Hyperparameter Tuning. After experimenting our methodology and performing a comparative analysis with previous studies from the literature, the obtained results showed the efficiency of our novel model, with Mean Squared Error (MSE) equals to 0.000112 and a Determination Coefficient (R) equals to 0.96470. It outperformed other models of the literature for predicting energy use based on real-world data collected from the campus of Georgia Tech in Atlanta, USA.

Abstract

This research presents a machine learning-based context-driven collaborative filtering approach with three
steps: contextual clustering, weighted similarity assessment, and collaborative filtering. User data is clustered
across 3 aspects, and similarity scores are calculated, dynamically weighted, and aggregated into a normalized
User-User similarity matrix. Collaborative filtering is then applied to generate contextual recommendations.
Experiments on the LDOS-CoMoDa dataset demonstrated good performance, with RMSE and MAE rates of
0.5774 and 0.3333 respectively, outperforming reference approaches.

Abstract

Vehicle-as-a-Service (VaaS) refers to on-demand rides and the sharing/offering of various kinds of intelligent transportation facilities (e.g., smart buses, electric vehicles, autonomous cars) to move from a source to a destination across one or several regions. Coupled with smart transportation systems—which are critical for addressing road network issues such as traffic congestion, parking shortages, and safety concerns—VaaS is increasingly being adopted in smart cities.
For example, a user may specify their needs in terms of source and destination stations, time and cost constraints, as well as preferred transportation modes and services (e.g., only connected buses and cars). Such a user profile is evaluated against available VaaS options under current and anticipated urban network conditions. However, current solutions do not support the customization of VaaS compositions and often treat user requests as traditional vehicle routing problems. In a smart mobility context, however, processing VaaS requests involves not only finding the optimal transportation path that meets user constraints (e.g., time, cost, transfer stations) but also selecting the top-rated combination of available VaaSs (e.g., a sequence of smart buses) with respect to the user profile (e.g., connectivity needs, specific facilities) and the quality of smart urban services.
To address these challenges, the goal of this paper is to develop a multi-modal recommender system that enables the personalized selection, composition, and scheduling of VaaS services while optimizing trip constraints (e.g., minimizing trip time and cost, and maximizing VaaS availability and reputation).
As a multi-population technique, Composite Particle Swarm Optimization (CPSO) is applied to aggregate the optimal set of high-quality and high-coverage VaaSs with respect to the requested trip. The regions composing the trip are explored using a modified A* search algorithm to find the optimal local (partial) path in each traversed region of the smart urban network. Comparative experiments involving two metaheuristics, a greedy algorithm, and a fuzzy clustering technique demonstrate the efficiency and superiority of our CPSO-based approach, achieving approximately a 28% improvement over its closest competitors.

Abstract

Abstract

As the adoption of unmanned aerial vehicles by both consumers and companies is growing rapidly, the use of drones is nowadays leading the way various types of packages (e.g., food, medication, supplies) are delivered. Drone services have increased companies’ benefits and saved money on their shipping costs, which resulted in a reduced delivery time and cost for consumers. However, the delivery tasks either achieved by single or swarming drones are facing several challenges, which are mainly related to the modeling of drones’ skyway network, the uncertainty of flight conditions and available resources, the consumers’ trust and quality of experience, the privacy concerns caused by shared user information (e.g., GPS, camera). Among these issues, we focus on the modeling and resource availability issues. To cope with complex delivery requests, this paper takes advantage of the service computing paradigm to propose a service composition approach, in which multiple drone services can participate in the delivery plan. We first propose a graph-based modeling of the skyway network and flights history. This latter, is fed into a proposed frequent subgraph mining algorithm, and is processed to extract relevant patterns from the previously generated delivery paths, based on consumers’ positive feedback. We also adopt Long Short-Term Memory to propose a model that forecasts the overload of charging stations, with the goal of tuning the mined (i.e., frequently traversed) paths with the low-congestion stations. The prediction and mining results are, finally, exploited, in the selection of the appropriate drone formation that will achieve the delivery mission. Experimental studies based on real-world datasets have confirmed the efficiency of our approach, compared to three graph-based approaches.